Matthias Seifert – författare

This book highlights research on the behavioral biases affecting judgmental accuracy in judgmental forecasting and showcases the state-of-the-art in judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning and deep learning. However, research shows that in the majority of industry contexts, human judgment remains an indispensable component of the managerial forecasting process. This book discusses ways in which decision-makers can address human behavioral issues in judgmental forecasting. 

The book begins by introducing readers to the notion of human-machine interactions. This includes a look at the necessity of managerial judgment in situations where organizations commonly have algorithmic decision support models at their disposal.The remainder of the book is divided into three parts, with Part I focusing on the role of individual-level judgment in the design and utilization of algorithmic models. The respective chapters cover individual-level biases such as algorithm aversion, model selection criteria, model-judgment aggregation issues and implications for behavioral change. In turn, Part II addresses the role of collective judgments in predictive analytics. The chapters focus on issues related to talent spotting, performance-weighted aggregation, and the wisdom of timely crowds. Part III concludes the book by shedding light on the importance of contextual factors as critical determinants of forecasting performance. Its chapters discuss the usefulness of scenario analysis, the role of external factors in time series forecasting and introduce the idea of mindful organizing as an approach to creating more sustainable forecasting practices in organizations.

This book lays out a foundation and taxonomy for Behavioral Decision Analysis, featuring representative work across various domains. Traditional research in the domain of Decision Analysis has focused on the design and application of logically consistent tools to support decision makers during the process of structuring problem complexity, modeling uncertainty, generating predictions, eliciting preferences, and, ultimately, making better decisions. Two commonly held assumptions are that the decision maker’s cognitive belief system is fully accessible and that this system can be understood and formalized by trained analysts. However, in past years, an active line of research has emerged studying instances in which such assumptions may not hold. This book unites this community under the common theme of Behavioral Decision Analysis. The taxonomy used in this book categorizes research based on task focus (prediction or decision) and behavioral level (individual or group). Two theoretical lenses that lie at the interface between (1) normative and descriptive research, and (2) normative and prescriptive research are introduced. The book then proceeds to highlight representative works across the two lenses focused on individual and group-level decision making. Featuring various methodologies and applications, the book serves as a reference for researchers, students, and professionals across different disciplines with a common interest in Behavioral Decision Analysis.